Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay | Zendy

Chien-Yu Lu | Zendy; Chin-Wen Liao | Zendy; Hsun-Heng Tsai | Zendy

Open Access

Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay

Author(s) -

Chien-Yu Lu,

Chin-Wen Liao,

Hsun-Heng Tsai

Publication year - 2009

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2009/430158

Subject(s) - passivity , lipschitz continuity , discrete time and continuous time , control theory (sociology) , interval (graph theory) , mathematics , bounded function , artificial neural network , linear matrix inequality , computer science , range (aeronautics) , mathematical optimization , control (management) , mathematical analysis , statistics , combinatorics , artificial intelligence , electrical engineering , engineering , materials science , machine learning , composite material

This paper examines a passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on an appropriate type of Lyapunov functional, sufficient passivity conditions for the DRNNs are derived in terms of a family of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness and applicability

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research